Physics in Medicine & Biology

Anatomically adaptive feature-wise linear modulation for deep learning-based low-dose CT denoising

Objective. Low-dose computed tomography (LDCT) reduces radiation dose but, introduces heterogeneous noise due to different photon attenuation based on anatomical tissue. Most deep learning techniques assume uniform noise in LDCT and perform equal noise removal across different regions, leading to sub-optimal performance across different tissues. This work aims to design a physics-based framework that explicitly models region-dependent noise characteristics to improve LDCT noise removal. Approach. We propose an anatomically adaptive noise reduction framework. The proposed anatomically adaptive feature-wise linear modulation (FiLM) model consists of a U-Net architecture that integrates two complementary units: the global FiLM unit in the encoder, which modifies features globally based on global image statistics to remove overall noise, and the local FiLM unit in the decoder, which modifies features locally based on the type of anatomical tissue. This dual design enables the modeling of overall image noise characteristics in addition to the removal of local noise associated with each anatomical tissue. Main results. The model performance was evaluated using AAPM–Mayo Clinic LDCT dataset, and the trained model tested on the TCIA dataset. The proposed model outperformed all competing methods. Local noise analysis showed that noise removal was consistent across different anatomical regions, achieving 37.14% in the lung, 48.75% in soft tissue, and 35.18% in bone. Visual results also confirmed significant improvements in noise removal, preservation of structural details, and reduction of non-residual distortions. Furthermore, the model demonstrated its ability to generalize under domain shift. Significance. This work presents a framework for anatomically adapted noise removal by linking feature modification with the physical properties of noise in LDCT through a dual-modulation process for both general and tissue-related noise. The model achieves a balance between noise removal and preservation of anatomical detail, making it a robust approach to LDCT noise removal.

Spatial signal distribution learning for high-resolution 3D system matrix calibration in magnetic particle imaging

Objective. Three-dimensional (3D) high-resolution system matrices (HR-SMs) are essential for high-quality image reconstruction in magnetic particle imaging (MPI), but obtaining HR-SMs is time-consuming and costly. This study aims to develop a learning-based 3D SM calibration method to reduce the calibration workload and maintain reconstruction accuracy. Approach. We propose a spatial signal distribution learning method for fast calibration of 3D HR-SMs. Specifically, a channel-decoupled multi-path state space model (MPC-SSM) is designed. This method flattens the 3D SM into multiple complementary spatial sequences, and uses different traversal paths to capture anisotropy and long-range spatial dependencies. To improve efficiency, feature channels are divided into disjoint groups and assigned to path-specific SSMs, achieving efficient multi-path sequence modeling while reducing computational overhead. Main results. We evaluated this method on both simulated and real MPI datasets (including OpenMPI). The results show that under 2× and 4× upsampling, the normalized reconstruction error of MPC-SSM is lower than that of existing interpolation and deep learning methods, and the quality of downstream image reconstruction is improved. Significance. This work provides a scalable and practical solution for 3D HR-SM calibration and provides a general modeling strategy for structured 3D medical data.

Non-Contrast-Informed Low-Dose Multi-Phase Contrast-Enhanced CT Reconstruction via Local Optimal Transport

Multi-phase contrast-enhanced CT (CECT) is widely employed to capture the dynamic enhancement patterns and temporal evolution of organs and lesions. However, acquiring multiple phases increases radiation exposure and is inevitably accompanied by inter-phase misalignment and inconsistencies due to patient motion and the temporal variations in contrast uptake. Further dose reduction exacerbates noise and streak artifacts, severely degrading image quality and diagnostic reliability. In this work, we propose a novel reconstruction framework for multi-phase low-dose CECT that is guided by a routinely acquired non-contrast CT scan under weakly paired conditions. Specifically, the reconstruction model was formulated that explicitly separates common anatomical structures from phase-specific contrast variations and noise by deep dictionary representations. Then we employ a proximal gradient optimization method, analytically deriving its iterative procedure and unfolding it into an end-to-end trainable architecture, which preserves the theoretical interpretability of the model and facilitates efficient inference. To enhance structural alignment, we integrate local optimal transport to establish anatomically meaningful correspondences across phases, thereby enforcing structural fidelity and radiodensity consistency. Extensive experiments on real clinical multi-phase datasets demonstrate that our method effectively suppresses noise and streak artifacts while recovering fine contrast-enhanced details. Both quantitative evaluation and expert clinical assessment confirm its superior performance compared with existing approaches. Moreover, downstream evaluation using the TotalSegmentator liver-lesion model shows substantial gains in hepatic tumor detectability under reduced-dose settings, enabling reliable lesion identification while significantly lowering radiation exposure. The codes and models are available at https://github.com/lixing0810/LOT-NCIRecon

Physics in Medicine & Biology

FS-DANet: Dual-Domain Signal Enhancement and Dynamic Spatial Calibration for Gastric Ultrasound Artifact Mitigation.

AI-Enhanced Hybrid Optimization and Deep Network Architecture for Noise Reduction in Low-Dose Medical Imaging

Low-dose computed tomography (LDCT) is a significant non-invasive imaging modality for disease diagnosis in early stages and clinical oncology. However, the reduction of radiation dose unavoidably introduces severe quantum noise, photon starvation and Poisson-Gaussian noise, which degrade contrast-to-noise ratio (CNR) and obscure subtle anatomical details. Recent advances in Artificial Intelligence (AI) have shown great promise in medical image restoration. However, pure deep learning methods often suffer from over-smoothing of fine structures and poor interpretability, while traditional non-convex variational models can preserve global edges, but are sensitive to the choice of parameters and produce staircasing artifacts. We propose an AI-empowered hybrid restoration framework that combines non-convex Total Variation (TV) optimization and a deep convolutional residual network within the Plug-and-Play (PnP) Alternating Direction Method of Multipliers (ADMM) framework to enjoy the complementary merits of the two paradigms. The AI based deep residual network can learn complex noise features and image priors efficiently. The optimization part keeps the structural fidelity and ensures the stable reconstruction. The proposed framework is tested on clinical lung CT slices from LIDC-IDRI benchmark dataset, and the experimental results show that the proposed framework achieves 33.97dB of Peak Signal-to-Noise Ratio (PSNR) and 0.918 of Structural Similarity Index Measure (SSIM) at noise level of σ=25. The experimental results show that the proposed AI-enabled hybrid model can better preserve structure edges, recover fine anatomical details and suppress noise compared with the traditional optimization methods and deep learning alone, which shows the effectiveness for low-dose medical image denoising.